Journal article
Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow
This article describes the application of neural networks for the design optimization of a curved wind turbine blade using an aero-elastic simulator with synthetic inflow turbulence. A vortex particle method where the wind turbine blades are represented by lifting-line theory is used, while the wind turbine structural dynamics are modeled using a finite-element multi-body based approach.
A neural network together with a gradient-based optimizer allows to quickly design a new curved wind turbine blade in a complex aero-elastic wind-turbine simulation scenario. The blade design found from the neural network has increased pre-bend and sweep compared to the straight blade design. It produces approximately 1% more power on average with a slight increase of mean thrust on the rotor of 0.02% compared to the straight one.
This study demonstrates that neural networks can be effective for designing wind turbine rotor blades involving complex aero-elastic simulation scenarios with turbulent inflow conditions. Further work may improve the performance of the neural network's predictive capabilities as well as the optimized design.
Language: | English |
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Year: | 2020 |
Pages: | 1524-1535 |
ISSN: | 18790682 and 09601481 |
Types: | Journal article |
DOI: | 10.1016/j.renene.2019.07.046 |
ORCIDs: | Sessarego, Matias , Feng, Ju , Ramos García, Néstor and Horcas, Sergio González |